Overview

Dataset statistics

Number of variables18
Number of observations2325
Missing cells1952
Missing cells (%)4.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory327.1 KiB
Average record size in memory144.1 B

Variable types

Numeric18

Alerts

quarter is highly correlated with preSnapHomeScore and 1 other fieldsHigh correlation
preSnapHomeScore is highly correlated with quarterHigh correlation
preSnapVisitorScore is highly correlated with quarterHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
quarter is highly correlated with preSnapHomeScore and 1 other fieldsHigh correlation
preSnapHomeScore is highly correlated with quarterHigh correlation
preSnapVisitorScore is highly correlated with quarterHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
quarter is highly correlated with preSnapHomeScore and 1 other fieldsHigh correlation
preSnapHomeScore is highly correlated with quarterHigh correlation
preSnapVisitorScore is highly correlated with quarterHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
quarter is highly correlated with preSnapHomeScore and 1 other fieldsHigh correlation
preSnapHomeScore is highly correlated with quarter and 1 other fieldsHigh correlation
preSnapVisitorScore is highly correlated with quarter and 1 other fieldsHigh correlation
s is highly correlated with disHigh correlation
dis is highly correlated with sHigh correlation
penaltyYards has 1915 (82.4%) missing values Missing
kickReturnYardage has 36 (1.5%) missing values Missing
preSnapHomeScore has 552 (23.7%) zeros Zeros
preSnapVisitorScore has 616 (26.5%) zeros Zeros
kickReturnYardage has 303 (13.0%) zeros Zeros

Reproduction

Analysis started2021-12-06 18:13:50.052749
Analysis finished2021-12-06 18:14:29.107640
Duration39.05 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

quarter
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.407741935
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:29.158598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.112721897
Coefficient of variation (CV)0.4621433387
Kurtosis-1.21144461
Mean2.407741935
Median Absolute Deviation (MAD)1
Skewness0.1671139984
Sum5598
Variance1.238150019
MonotonicityNot monotonic
2021-12-06T13:14:29.249610image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
1630
27.1%
2629
27.1%
3570
24.5%
4480
20.6%
516
 
0.7%
ValueCountFrequency (%)
1630
27.1%
2629
27.1%
3570
24.5%
4480
20.6%
516
 
0.7%
ValueCountFrequency (%)
516
 
0.7%
4480
20.6%
3570
24.5%
2629
27.1%
1630
27.1%

yardsToGo
Real number (ℝ≥0)

Distinct34
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.837849462
Minimum1
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:29.390608image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q314
95-th percentile21
Maximum46
Range45
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.990449558
Coefficient of variation (CV)0.6089186037
Kurtosis0.9791386053
Mean9.837849462
Median Absolute Deviation (MAD)4
Skewness0.871370086
Sum22873
Variance35.88548591
MonotonicityNot monotonic
2021-12-06T13:14:29.550172image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10195
 
8.4%
7174
 
7.5%
5166
 
7.1%
6164
 
7.1%
8159
 
6.8%
9130
 
5.6%
4130
 
5.6%
11127
 
5.5%
3121
 
5.2%
15105
 
4.5%
Other values (24)854
36.7%
ValueCountFrequency (%)
195
4.1%
299
4.3%
3121
5.2%
4130
5.6%
5166
7.1%
6164
7.1%
7174
7.5%
8159
6.8%
9130
5.6%
10195
8.4%
ValueCountFrequency (%)
461
 
< 0.1%
411
 
< 0.1%
323
 
0.1%
312
 
0.1%
301
 
< 0.1%
294
 
0.2%
2810
0.4%
273
 
0.1%
2610
0.4%
259
0.4%

preSnapHomeScore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.5372043
Minimum0
Maximum54
Zeros552
Zeros (%)23.7%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:29.661125image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q317
95-th percentile28
Maximum54
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.472989771
Coefficient of variation (CV)0.8990040907
Kurtosis0.3601721019
Mean10.5372043
Median Absolute Deviation (MAD)7
Skewness0.865159728
Sum24499
Variance89.73753521
MonotonicityNot monotonic
2021-12-06T13:14:29.798194image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0552
23.7%
7334
14.4%
10213
 
9.2%
3196
 
8.4%
14169
 
7.3%
17123
 
5.3%
2484
 
3.6%
1382
 
3.5%
2177
 
3.3%
2071
 
3.1%
Other values (31)424
18.2%
ValueCountFrequency (%)
0552
23.7%
3196
 
8.4%
51
 
< 0.1%
664
 
2.8%
7334
14.4%
84
 
0.2%
940
 
1.7%
10213
 
9.2%
116
 
0.3%
1213
 
0.6%
ValueCountFrequency (%)
541
 
< 0.1%
481
 
< 0.1%
451
 
< 0.1%
442
 
0.1%
431
 
< 0.1%
424
 
0.2%
408
0.3%
3810
0.4%
363
 
0.1%
3511
0.5%

preSnapVisitorScore
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.854193548
Minimum0
Maximum51
Zeros616
Zeros (%)26.5%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:29.916311image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q316
95-th percentile28
Maximum51
Range51
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.423246688
Coefficient of variation (CV)0.9562676684
Kurtosis0.5279840771
Mean9.854193548
Median Absolute Deviation (MAD)7
Skewness0.9533596458
Sum22911
Variance88.79757815
MonotonicityNot monotonic
2021-12-06T13:14:30.038537image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0616
26.5%
7303
13.0%
3227
 
9.8%
10164
 
7.1%
17133
 
5.7%
14127
 
5.5%
13103
 
4.4%
695
 
4.1%
2080
 
3.4%
2164
 
2.8%
Other values (31)413
17.8%
ValueCountFrequency (%)
0616
26.5%
24
 
0.2%
3227
 
9.8%
695
 
4.1%
7303
13.0%
84
 
0.2%
943
 
1.8%
10164
 
7.1%
1215
 
0.6%
13103
 
4.4%
ValueCountFrequency (%)
511
 
< 0.1%
472
 
0.1%
453
 
0.1%
426
 
0.3%
413
 
0.1%
402
 
0.1%
391
 
< 0.1%
385
 
0.2%
372
 
0.1%
3516
0.7%

snapTime
Real number (ℝ≥0)

Distinct52
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8589591398
Minimum0.59
Maximum1.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:30.160676image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.59
5-th percentile0.76
Q10.82
median0.86
Q30.9
95-th percentile0.96
Maximum1.19
Range0.6
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.06367300662
Coefficient of variation (CV)0.07412809722
Kurtosis1.558181981
Mean0.8589591398
Median Absolute Deviation (MAD)0.04
Skewness-0.2635500674
Sum1997.08
Variance0.004054251772
MonotonicityNot monotonic
2021-12-06T13:14:30.289917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.87180
 
7.7%
0.85165
 
7.1%
0.88157
 
6.8%
0.86156
 
6.7%
0.83151
 
6.5%
0.89150
 
6.5%
0.84149
 
6.4%
0.91127
 
5.5%
0.9112
 
4.8%
0.82109
 
4.7%
Other values (42)869
37.4%
ValueCountFrequency (%)
0.591
 
< 0.1%
0.62
 
0.1%
0.622
 
0.1%
0.634
0.2%
0.644
0.2%
0.652
 
0.1%
0.667
0.3%
0.671
 
< 0.1%
0.684
0.2%
0.695
0.2%
ValueCountFrequency (%)
1.191
 
< 0.1%
1.111
 
< 0.1%
1.091
 
< 0.1%
1.081
 
< 0.1%
1.071
 
< 0.1%
1.063
0.1%
1.054
0.2%
1.043
0.1%
1.032
0.1%
1.024
0.2%

hangTime
Real number (ℝ≥0)

Distinct235
Distinct (%)10.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4.324810671
Minimum2.43
Maximum5.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:30.425964image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2.43
5-th percentile3.43
Q14.08
median4.4
Q34.65
95-th percentile4.96
Maximum5.43
Range3
Interquartile range (IQR)0.57

Descriptive statistics

Standard deviation0.4534893732
Coefficient of variation (CV)0.1048576244
Kurtosis0.3845773096
Mean4.324810671
Median Absolute Deviation (MAD)0.27
Skewness-0.6953396428
Sum10050.86
Variance0.2056526116
MonotonicityNot monotonic
2021-12-06T13:14:30.555796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4532
 
1.4%
4.5832
 
1.4%
4.4131
 
1.3%
4.6631
 
1.3%
4.4431
 
1.3%
4.6531
 
1.3%
4.5928
 
1.2%
4.6227
 
1.2%
4.5227
 
1.2%
4.3926
 
1.1%
Other values (225)2028
87.2%
ValueCountFrequency (%)
2.431
< 0.1%
2.761
< 0.1%
2.781
< 0.1%
2.831
< 0.1%
2.841
< 0.1%
2.881
< 0.1%
2.961
< 0.1%
2.982
0.1%
2.992
0.1%
3.012
0.1%
ValueCountFrequency (%)
5.431
 
< 0.1%
5.41
 
< 0.1%
5.382
0.1%
5.321
 
< 0.1%
5.31
 
< 0.1%
5.291
 
< 0.1%
5.281
 
< 0.1%
5.241
 
< 0.1%
5.224
0.2%
5.211
 
< 0.1%

operationTime
Real number (ℝ≥0)

Distinct71
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.135191398
Minimum1.66
Maximum4.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:30.689830image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1.66
5-th percentile1.98
Q12.07
median2.14
Q32.2
95-th percentile2.29
Maximum4.92
Range3.26
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.1167489175
Coefficient of variation (CV)0.05467843193
Kurtosis148.6396688
Mean2.135191398
Median Absolute Deviation (MAD)0.06
Skewness6.491344608
Sum4964.32
Variance0.01363030974
MonotonicityNot monotonic
2021-12-06T13:14:30.812829image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.12106
 
4.6%
2.16104
 
4.5%
2.19101
 
4.3%
2.17100
 
4.3%
2.1898
 
4.2%
2.1595
 
4.1%
2.1495
 
4.1%
2.0794
 
4.0%
2.1390
 
3.9%
2.0990
 
3.9%
Other values (61)1352
58.2%
ValueCountFrequency (%)
1.662
 
0.1%
1.681
 
< 0.1%
1.71
 
< 0.1%
1.771
 
< 0.1%
1.82
 
0.1%
1.822
 
0.1%
1.832
 
0.1%
1.842
 
0.1%
1.854
0.2%
1.865
0.2%
ValueCountFrequency (%)
4.921
 
< 0.1%
3.571
 
< 0.1%
2.711
 
< 0.1%
2.461
 
< 0.1%
2.452
 
0.1%
2.431
 
< 0.1%
2.421
 
< 0.1%
2.43
0.1%
2.392
 
0.1%
2.385
0.2%

kickLength
Real number (ℝ≥0)

Distinct48
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.4572043
Minimum5
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:30.994828image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile38
Q145
median50
Q354
95-th percentile60
Maximum79
Range74
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.008999373
Coefficient of variation (CV)0.141718471
Kurtosis0.5882158993
Mean49.4572043
Median Absolute Deviation (MAD)5
Skewness-0.1489800925
Sum114988
Variance49.12607222
MonotonicityNot monotonic
2021-12-06T13:14:31.109831image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
50132
 
5.7%
51129
 
5.5%
52125
 
5.4%
49123
 
5.3%
47119
 
5.1%
55115
 
4.9%
53113
 
4.9%
48111
 
4.8%
54108
 
4.6%
46107
 
4.6%
Other values (38)1143
49.2%
ValueCountFrequency (%)
51
 
< 0.1%
211
 
< 0.1%
251
 
< 0.1%
281
 
< 0.1%
292
 
0.1%
303
 
0.1%
313
 
0.1%
329
0.4%
334
0.2%
345
0.2%
ValueCountFrequency (%)
791
 
< 0.1%
741
 
< 0.1%
712
 
0.1%
693
 
0.1%
684
 
0.2%
672
 
0.1%
666
 
0.3%
6510
0.4%
649
0.4%
6322
0.9%

penaltyYards
Real number (ℝ)

MISSING

Distinct19
Distinct (%)4.6%
Missing1915
Missing (%)82.4%
Infinite0
Infinite (%)0.0%
Mean5.229268293
Minimum-15
Maximum15
Zeros17
Zeros (%)0.7%
Negative74
Negative (%)3.2%
Memory size18.3 KiB
2021-12-06T13:14:31.214827image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-15
Q15
median10
Q310
95-th percentile10.55
Maximum15
Range30
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.552791446
Coefficient of variation (CV)1.635561797
Kurtosis0.7764511146
Mean5.229268293
Median Absolute Deviation (MAD)0
Skewness-1.475690808
Sum2144
Variance73.15024152
MonotonicityNot monotonic
2021-12-06T13:14:31.294826image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10225
 
9.7%
-1541
 
1.8%
017
 
0.7%
-517
 
0.7%
516
 
0.7%
716
 
0.7%
-1016
 
0.7%
1513
 
0.6%
812
 
0.5%
910
 
0.4%
Other values (9)27
 
1.2%
(Missing)1915
82.4%
ValueCountFrequency (%)
-1541
1.8%
-1016
 
0.7%
-517
0.7%
017
0.7%
11
 
< 0.1%
23
 
0.1%
34
 
0.2%
43
 
0.1%
516
 
0.7%
68
 
0.3%
ValueCountFrequency (%)
1513
 
0.6%
142
 
0.1%
133
 
0.1%
122
 
0.1%
111
 
< 0.1%
10225
9.7%
910
 
0.4%
812
 
0.5%
716
 
0.7%
68
 
0.3%

s
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct590
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.600855914
Minimum0.03
Maximum8.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:31.403825image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile0.65
Q11.48
median2.39
Q33.48
95-th percentile5.348
Maximum8.21
Range8.18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.478855764
Coefficient of variation (CV)0.5686034955
Kurtosis0.3683822947
Mean2.600855914
Median Absolute Deviation (MAD)0.99
Skewness0.7850455052
Sum6046.99
Variance2.18701437
MonotonicityNot monotonic
2021-12-06T13:14:31.525240image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4913
 
0.6%
1.9712
 
0.5%
1.8512
 
0.5%
1.8612
 
0.5%
1.212
 
0.5%
2.4512
 
0.5%
1.9312
 
0.5%
2.511
 
0.5%
2.4811
 
0.5%
2.5711
 
0.5%
Other values (580)2207
94.9%
ValueCountFrequency (%)
0.031
 
< 0.1%
0.061
 
< 0.1%
0.072
0.1%
0.092
0.1%
0.11
 
< 0.1%
0.111
 
< 0.1%
0.132
0.1%
0.153
0.1%
0.161
 
< 0.1%
0.181
 
< 0.1%
ValueCountFrequency (%)
8.211
< 0.1%
7.971
< 0.1%
7.831
< 0.1%
7.71
< 0.1%
7.661
< 0.1%
7.591
< 0.1%
7.481
< 0.1%
7.441
< 0.1%
7.431
< 0.1%
7.351
< 0.1%

a
Real number (ℝ≥0)

Distinct513
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.498378495
Minimum0.08
Maximum7.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:31.662241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.67
Q11.55
median2.37
Q33.33
95-th percentile4.72
Maximum7.99
Range7.91
Interquartile range (IQR)1.78

Descriptive statistics

Standard deviation1.24484548
Coefficient of variation (CV)0.4982613655
Kurtosis0.1664079923
Mean2.498378495
Median Absolute Deviation (MAD)0.87
Skewness0.5429801758
Sum5808.73
Variance1.54964027
MonotonicityNot monotonic
2021-12-06T13:14:31.786239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.4913
 
0.6%
2.613
 
0.6%
2.7113
 
0.6%
1.8413
 
0.6%
2.2113
 
0.6%
2.5512
 
0.5%
2.2812
 
0.5%
1.7712
 
0.5%
1.512
 
0.5%
3.0511
 
0.5%
Other values (503)2201
94.7%
ValueCountFrequency (%)
0.081
 
< 0.1%
0.131
 
< 0.1%
0.181
 
< 0.1%
0.211
 
< 0.1%
0.222
 
0.1%
0.245
0.2%
0.262
 
0.1%
0.283
0.1%
0.35
0.2%
0.325
0.2%
ValueCountFrequency (%)
7.991
< 0.1%
7.861
< 0.1%
7.381
< 0.1%
7.081
< 0.1%
6.921
< 0.1%
6.791
< 0.1%
6.491
< 0.1%
6.461
< 0.1%
6.371
< 0.1%
6.191
< 0.1%

dis
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct80
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2623354839
Minimum0
Maximum0.83
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:31.914239image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.07
Q10.15
median0.24
Q30.35
95-th percentile0.54
Maximum0.83
Range0.83
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.148993494
Coefficient of variation (CV)0.567950213
Kurtosis0.360505285
Mean0.2623354839
Median Absolute Deviation (MAD)0.1
Skewness0.7938787903
Sum609.93
Variance0.02219906124
MonotonicityNot monotonic
2021-12-06T13:14:32.033238image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1574
 
3.2%
0.1873
 
3.1%
0.1273
 
3.1%
0.2267
 
2.9%
0.2467
 
2.9%
0.1966
 
2.8%
0.2665
 
2.8%
0.2564
 
2.8%
0.362
 
2.7%
0.2162
 
2.7%
Other values (70)1652
71.1%
ValueCountFrequency (%)
02
 
0.1%
0.018
 
0.3%
0.024
 
0.2%
0.0319
 
0.8%
0.0420
 
0.9%
0.0528
1.2%
0.0629
1.2%
0.0742
1.8%
0.0853
2.3%
0.0957
2.5%
ValueCountFrequency (%)
0.831
 
< 0.1%
0.81
 
< 0.1%
0.791
 
< 0.1%
0.781
 
< 0.1%
0.763
0.1%
0.744
0.2%
0.731
 
< 0.1%
0.725
0.2%
0.712
 
0.1%
0.74
0.2%

o
Real number (ℝ≥0)

Distinct2184
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.4206194
Minimum0.76
Maximum359.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:32.159281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.76
5-th percentile63.506
Q197.32
median229.49
Q3276.89
95-th percentile312.592
Maximum359.64
Range358.88
Interquartile range (IQR)179.57

Descriptive statistics

Standard deviation94.2188423
Coefficient of variation (CV)0.4947932772
Kurtosis-1.632914015
Mean190.4206194
Median Absolute Deviation (MAD)85.12
Skewness-0.08867546704
Sum442727.94
Variance8877.190245
MonotonicityNot monotonic
2021-12-06T13:14:32.343281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
909
 
0.4%
85.93
 
0.1%
110.83
 
0.1%
272.443
 
0.1%
80.383
 
0.1%
285.993
 
0.1%
83.023
 
0.1%
280.323
 
0.1%
262.392
 
0.1%
266.262
 
0.1%
Other values (2174)2291
98.5%
ValueCountFrequency (%)
0.761
< 0.1%
0.811
< 0.1%
1.011
< 0.1%
2.331
< 0.1%
5.431
< 0.1%
5.561
< 0.1%
5.681
< 0.1%
10.411
< 0.1%
11.681
< 0.1%
12.51
< 0.1%
ValueCountFrequency (%)
359.641
< 0.1%
358.811
< 0.1%
358.711
< 0.1%
357.861
< 0.1%
356.32
0.1%
353.931
< 0.1%
352.051
< 0.1%
351.871
< 0.1%
351.081
< 0.1%
350.491
< 0.1%

dir
Real number (ℝ≥0)

Distinct2250
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177.8044946
Minimum0.11
Maximum359.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:32.478281image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.11
5-th percentile13.752
Q189.03
median176.18
Q3269.41
95-th percentile343.068
Maximum359.97
Range359.86
Interquartile range (IQR)180.38

Descriptive statistics

Standard deviation105.043187
Coefficient of variation (CV)0.5907791433
Kurtosis-1.156141084
Mean177.8044946
Median Absolute Deviation (MAD)89.78
Skewness0.01010756195
Sum413395.45
Variance11034.07114
MonotonicityNot monotonic
2021-12-06T13:14:32.618245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
312.923
 
0.1%
193.832
 
0.1%
21.592
 
0.1%
301.42
 
0.1%
181.472
 
0.1%
227.382
 
0.1%
253.612
 
0.1%
319.862
 
0.1%
323.952
 
0.1%
8.222
 
0.1%
Other values (2240)2304
99.1%
ValueCountFrequency (%)
0.111
< 0.1%
0.151
< 0.1%
0.171
< 0.1%
0.221
< 0.1%
0.251
< 0.1%
0.31
< 0.1%
0.471
< 0.1%
0.831
< 0.1%
1.211
< 0.1%
1.491
< 0.1%
ValueCountFrequency (%)
359.971
< 0.1%
359.911
< 0.1%
359.661
< 0.1%
359.551
< 0.1%
359.361
< 0.1%
358.741
< 0.1%
358.61
< 0.1%
358.571
< 0.1%
358.181
< 0.1%
357.541
< 0.1%

distance_returner_gunner1
Real number (ℝ≥0)

Distinct2323
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.13486033
Minimum0.3448188
Maximum58.13506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:32.752920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.3448188
5-th percentile3.38416266
Q18.6688
median14.464415
Q321.653605
95-th percentile34.8210128
Maximum58.13506
Range57.7902412
Interquartile range (IQR)12.984805

Descriptive statistics

Standard deviation9.841987583
Coefficient of variation (CV)0.6099828185
Kurtosis1.025811555
Mean16.13486033
Median Absolute Deviation (MAD)6.3114275
Skewness0.9664190448
Sum37513.55028
Variance96.86471959
MonotonicityNot monotonic
2021-12-06T13:14:32.873955image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.7087162
 
0.1%
4.63405852
 
0.1%
15.0187121
 
< 0.1%
11.9509411
 
< 0.1%
15.7641
 
< 0.1%
7.2001111
 
< 0.1%
15.7299711
 
< 0.1%
20.4690631
 
< 0.1%
6.20208021
 
< 0.1%
7.2038671
 
< 0.1%
Other values (2313)2313
99.5%
ValueCountFrequency (%)
0.34481881
< 0.1%
0.353553381
< 0.1%
0.50635961
< 0.1%
0.576974871
< 0.1%
0.68476271
< 0.1%
0.77369241
< 0.1%
0.892692571
< 0.1%
0.92444581
< 0.1%
0.95519631
< 0.1%
1.00463931
< 0.1%
ValueCountFrequency (%)
58.135061
< 0.1%
57.8839571
< 0.1%
56.9281
< 0.1%
55.9755251
< 0.1%
54.844621
< 0.1%
54.32221
< 0.1%
52.865211
< 0.1%
51.7384341
< 0.1%
51.6355931
< 0.1%
51.2346531
< 0.1%

distance_returner_gunner2
Real number (ℝ≥0)

Distinct2323
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.86617154
Minimum0.3324154
Maximum70.35642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2021-12-06T13:14:33.005920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0.3324154
5-th percentile3.06168892
Q18.680973
median14.288631
Q321.139584
95-th percentile34.2725172
Maximum70.35642
Range70.0240046
Interquartile range (IQR)12.458611

Descriptive statistics

Standard deviation9.62495462
Coefficient of variation (CV)0.6066337172
Kurtosis1.246311258
Mean15.86617154
Median Absolute Deviation (MAD)6.064168
Skewness0.9656266679
Sum36888.84883
Variance92.63975143
MonotonicityNot monotonic
2021-12-06T13:14:33.129999image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.61830042
 
0.1%
4.3796122
 
0.1%
26.9253431
 
< 0.1%
13.8451041
 
< 0.1%
13.6711561
 
< 0.1%
22.9653531
 
< 0.1%
11.3981621
 
< 0.1%
17.284241
 
< 0.1%
3.25884941
 
< 0.1%
7.9450741
 
< 0.1%
Other values (2313)2313
99.5%
ValueCountFrequency (%)
0.33241541
< 0.1%
0.40311291
< 0.1%
0.42720021
< 0.1%
0.451884961
< 0.1%
0.58008621
< 0.1%
0.600083351
< 0.1%
0.61032781
< 0.1%
0.61294371
< 0.1%
0.71554181
< 0.1%
0.73925641
< 0.1%
ValueCountFrequency (%)
70.356421
< 0.1%
58.6617241
< 0.1%
58.2401121
< 0.1%
57.850541
< 0.1%
56.496231
< 0.1%
54.0531581
< 0.1%
51.0582541
< 0.1%
50.7634541
< 0.1%
50.7629051
< 0.1%
48.4307171
< 0.1%

y_football_rescaled
Real number (ℝ)

Distinct1474
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.64695484
Minimum-31.52
Maximum100
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size18.3 KiB
2021-12-06T13:14:33.259998image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-31.52
5-th percentile15.142
Q139.44
median59.92
Q379.36
95-th percentile96.47
Maximum100
Range131.52
Interquartile range (IQR)39.92

Descriptive statistics

Standard deviation25.25680121
Coefficient of variation (CV)0.430658357
Kurtosis-0.8544461636
Mean58.64695484
Median Absolute Deviation (MAD)19.93
Skewness-0.2339598842
Sum136354.17
Variance637.9060075
MonotonicityNot monotonic
2021-12-06T13:14:33.378997image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.536
 
0.3%
43.196
 
0.3%
76.216
 
0.3%
78.766
 
0.3%
78.165
 
0.2%
59.555
 
0.2%
97.825
 
0.2%
84.095
 
0.2%
72.915
 
0.2%
77.415
 
0.2%
Other values (1464)2271
97.7%
ValueCountFrequency (%)
-31.521
< 0.1%
0.561
< 0.1%
1.21
< 0.1%
1.311
< 0.1%
2.291
< 0.1%
2.661
< 0.1%
2.931
< 0.1%
3.081
< 0.1%
3.111
< 0.1%
3.491
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
99.921
 
< 0.1%
99.892
0.1%
99.853
0.1%
99.774
0.2%
99.741
 
< 0.1%
99.73
0.1%
99.621
 
< 0.1%
99.591
 
< 0.1%
99.551
 
< 0.1%

kickReturnYardage
Real number (ℝ)

MISSING
ZEROS

Distinct77
Distinct (%)3.4%
Missing36
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean8.677588467
Minimum-13
Maximum99
Zeros303
Zeros (%)13.0%
Negative121
Negative (%)5.2%
Memory size18.3 KiB
2021-12-06T13:14:33.504989image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile-1
Q12
median7
Q312
95-th percentile23
Maximum99
Range112
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.56684641
Coefficient of variation (CV)1.217716933
Kurtosis18.01192287
Mean8.677588467
Median Absolute Deviation (MAD)5
Skewness3.371208688
Sum19863
Variance111.6582431
MonotonicityNot monotonic
2021-12-06T13:14:33.624722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0303
 
13.0%
8140
 
6.0%
6130
 
5.6%
5120
 
5.2%
1118
 
5.1%
9116
 
5.0%
7110
 
4.7%
10108
 
4.6%
11104
 
4.5%
2102
 
4.4%
Other values (67)938
40.3%
ValueCountFrequency (%)
-131
 
< 0.1%
-101
 
< 0.1%
-82
 
0.1%
-66
 
0.3%
-55
 
0.2%
-48
 
0.3%
-319
 
0.8%
-233
 
1.4%
-146
 
2.0%
0303
13.0%
ValueCountFrequency (%)
991
 
< 0.1%
911
 
< 0.1%
881
 
< 0.1%
851
 
< 0.1%
842
0.1%
831
 
< 0.1%
782
0.1%
742
0.1%
732
0.1%
713
0.1%

Interactions

2021-12-06T13:14:26.453714image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:52.483980image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:54.600415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:56.530957image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:58.462304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:00.602111image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:02.561616image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:04.630348image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:06.685208image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:08.727918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:10.503899image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:12.515473image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:14.540283image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:16.395599image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:18.373906image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:20.384408image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:22.330011image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:24.446167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:26.566352image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:52.647006image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:54.704664image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:56.635972image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:58.595270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:00.724076image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:02.673616image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:04.747361image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:06.795263image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:08.830918image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:10.619898image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:12.626439image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:14.643282image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:16.512650image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:18.483920image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:20.502452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:22.456010image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:24.559173image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:26.658314image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:52.744770image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:54.794673image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:56.729017image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:13:58.708270image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:00.819077image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:02.769242image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:04.848421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:06.892214image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:08.920925image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:10.736900image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:12.784064image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:14.735282image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:16.608245image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:18.581902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-06T13:14:20.597484image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2021-12-06T13:14:14.160198image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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2021-12-06T13:14:26.345661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2021-12-06T13:14:33.806769image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-06T13:14:34.027753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-06T13:14:34.254014image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-06T13:14:34.472756image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-06T13:14:28.460872image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-06T13:14:28.731637image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-12-06T13:14:28.899602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-12-06T13:14:28.991602image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

quarteryardsToGopreSnapHomeScorepreSnapVisitorScoresnapTimehangTimeoperationTimekickLengthpenaltyYardssadisodirdistance_returner_gunner1distance_returner_gunner2y_football_rescaledkickReturnYardage
0111000.954.172.2452NaN2.183.390.21289.7027.8510.4225387.94507438.6914.0
127030.854.222.2147NaN2.182.230.2298.2463.9610.3489236.03623262.8912.0
24614200.894.562.3060NaN2.823.610.28267.41175.5314.71823711.81568946.8316.0
3210030.883.392.1543NaN3.262.060.33283.94306.7416.22007836.45609783.9014.0
42111470.893.192.1639NaN2.194.930.2376.2348.5927.61014713.61609734.2212.0
52714100.873.832.2343NaN2.803.130.29271.22289.958.05588643.21179222.598.0
6263200.834.452.1650NaN0.452.820.06282.02158.455.30396115.98739582.039.0
7220070.773.922.0639NaN1.512.630.15272.0042.805.48178814.04752638.914.0
832214170.864.282.1855NaN2.392.950.24315.4876.9422.99990711.03402425.9712.0
941960.804.732.0464NaN2.287.860.20295.13229.1212.6204801.7938789.3410.0

Last rows

quarteryardsToGopreSnapHomeScorepreSnapVisitorScoresnapTimehangTimeoperationTimekickLengthpenaltyYardssadisodirdistance_returner_gunner1distance_returner_gunner2y_football_rescaledkickReturnYardage
2315215780.954.122.1840NaN2.202.140.2296.90130.444.1431510.71554270.620.0
2316123000.854.652.0940NaN2.133.460.22281.05292.505.57778626.43823281.011.0
23173419200.803.872.1840NaN3.692.060.37153.95192.153.82627813.90270834.524.0
23182212600.903.891.9044NaN2.483.770.25127.00135.6619.20308319.40733139.3620.0
231928370.834.542.0545NaN1.244.850.11263.61245.8315.9307288.34748574.159.0
23203111090.824.062.14478.04.474.030.45120.48139.3628.84397333.4201809.531.0
23212231430.684.381.8954NaN0.414.730.05289.31103.6749.5256236.88035612.763.0
23223717290.984.032.1429NaN6.222.960.63110.50168.2512.9249606.7900309.49NaN
23232121030.664.631.8250NaN3.072.130.30284.3482.8712.2502339.08679351.292.0
2324214000.855.172.1657NaN1.851.960.1974.53284.2429.2814107.15856189.9812.0